{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113208"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113208","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Decision support system for earthmoving production estimates and equipment allocation","abstract":"Construction planners require in-depth production estimates in an area of operations to optimally deploy equipment to prepare an area to its end state. With the increasing use of satellite imagery and voxelized LiDAR data, digital elevation models can be utilized for these estimates. However, an automated program for production estimates based on area characteristics is currently unavailable. Using well-established production estimation techniques, a computerized decision-support program was created that can be applied to both commercial and military construction operations. The program conducts analyses for end-to-end activities from simple user inputs and automated calculations. Pre- and post-excavation activities have been largely ignored in the creation of previous decision-support systems. The emergence of machine-learning and voxel data analysis allow for this program to be utilized as a base for a fully automated estimation program, eliminating the need for user inputs. The advent of autonomous construction equipment allows for optimal deployment based on estimates provided by the end-to-end site preparation decision support system developed in this research paper.","abstract_html":"Construction planners require in-depth production estimates in an area of operations to optimally deploy equipment to prepare an area to its end state. With the increasing use of satellite imagery and voxelized LiDAR data, digital elevation models can be utilized for these estimates. However, an automated program for production estimates based on area characteristics is currently unavailable. Using well-established production estimation techniques, a computerized decision-support program was created that can be applied to both commercial and military construction operations. The program conducts analyses for end-to-end activities from simple user inputs and automated calculations. Pre- and post-excavation activities have been largely ignored in the creation of previous decision-support systems. The emergence of machine-learning and voxel data analysis allow for this program to be utilized as a base for a fully automated estimation program, eliminating the need for user inputs. The advent of autonomous construction equipment allows for optimal deployment based on estimates provided by the end-to-end site preparation decision support system developed in this research paper.","abstract_has_math":false,"creators":["Lewandowski, Jakub P."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Nagi, Rakesh","Norris, William Robert"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:35:18Z","date_published":"2022-01-12T22:35:18Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Construction","automation","excavation","LiDAR"],"languages":["en"],"rights":["Copyright 2021 Jakub Lewandowski"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113208","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Nagi, Rakesh","Norris, William Robert"]},{"key":"dc:creator","label":"Author","values":["Lewandowski, Jakub P."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:35:18Z","2024-01-12T22:35:30Z","2021-07-22","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems & Entrepreneurial Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Construction","automation","excavation","LiDAR"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Jakub Lewandowski"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113208"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Construction planners require in-depth production estimates in an area of operations to optimally deploy equipment to prepare an area to its end state. With the increasing use of satellite imagery and voxelized LiDAR data, digital elevation models can be utilized for these estimates. However, an automated program for production estimates based on area characteristics is currently unavailable. Using well-established production estimation techniques, a computerized decision-support program was created that can be applied to both commercial and military construction operations. The program conducts analyses for end-to-end activities from simple user inputs and automated calculations. Pre- and post-excavation activities have been largely ignored in the creation of previous decision-support systems. The emergence of machine-learning and voxel data analysis allow for this program to be utilized as a base for a fully automated estimation program, eliminating the need for user inputs. The advent of autonomous construction equipment allows for optimal deployment based on estimates provided by the end-to-end site preparation decision support system developed in this research paper.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Jakub Lewandowski, accepted the attached license on 2021-07-15 at 16:46.","The student, Jakub Lewandowski, submitted this Thesis for approval on 2021-07-15 at 16:51.","This Thesis was approved for publication on 2021-07-22 at 10:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16969 on 2022-01-12 at 12:55:23","Made available in DSpace on 2022-01-12T22:35:18Z (GMT). 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With the increasing use of satellite imagery and voxelized LiDAR data, digital elevation models can be utilized for these estimates. However, an automated program for production estimates based on area characteristics is currently unavailable. Using well-established production estimation techniques, a computerized decision-support program was created that can be applied to both commercial and military construction operations. The program conducts analyses for end-to-end activities from simple user inputs and automated calculations. Pre- and post-excavation activities have been largely ignored in the creation of previous decision-support systems. The emergence of machine-learning and voxel data analysis allow for this program to be utilized as a base for a fully automated estimation program, eliminating the need for user inputs. The advent of autonomous construction equipment allows for optimal deployment based on estimates provided by the end-to-end site preparation decision support system developed in this research paper.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Jakub Lewandowski, accepted the attached license on 2021-07-15 at 16:46.","The student, Jakub Lewandowski, submitted this Thesis for approval on 2021-07-15 at 16:51.","This Thesis was approved for publication on 2021-07-22 at 10:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16969 on 2022-01-12 at 12:55:23","Made available in DSpace on 2022-01-12T22:35:18Z (GMT). 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